The ability to collect and analyze information on consumer shopping behavior is valuable to a number of entities, such as product manufacturers, advertising agencies and stores. These entities use this data to determine behaviors or patterns of customers or consumers, such as how much time they spend shopping, what they buy, how often they stop, what displays they stop in front of, how many times they shop in the store before making a purchase, etc. These behaviors or patterns allow the entities to optimize their products, displays, advertising and promotions to improve product sales and, ultimately, profits. It also allows consumers to be segmented into categories related to their shopping behaviors and habits. A significant amount of money and effort is expended to collect and analyze such data.
Conventional techniques for identifying consumer market segments include, for example: market surveys; data mining credit card and customer loyalty card information; observer/counting shoppers and simple statistical analysis (e.g., X % of males buy Brand A tires every 12 months), etc. However, the are several potential drawbacks associated with such current market analysis approaches, such as: they are expensive and time consuming; data may not be integrated across stores, industries, regions and countries; not all customers may participate in the surveys; much of the information (such as number of times a customer visits before purchasing, time spent shopping in a store, etc) is difficult to determine; and it may be difficult to integrate information across market channels (e.g., stores, Internet, mail order).
Various systems have been developed in an attempt to more easily collect such information. One such example is set forth in U.S. Patent Pub. No. 2007/0067220 to Godsey et al., which is directed to a system for tracking a plurality of product containers in a store environment and generating a track through the store environment representative of a continuous path followed by each of the product containers to a point-of-sale location. The system includes the plurality of product containers and a plurality of identification tags, each of which is associated with and uniquely identifies one of the product containers. A plurality of sensors is provided in the store environment, each of which has a region associated therewith within which the identification tags are detected. At least one of the plurality of sensors has within its associated region the point-of-sale location. A processor is configured to receive location data from the plurality of sensors and generate the track therefrom.
Yet another similar system is set forth in U.S. Pat. No. 7,006,982 to Sorensen. This patent discloses a system and method for analyzing the behavior of a shopper within a shopping environment. The method determines the position of a product within the shopping environment, tracks a shopper path of a shopper through the shopping environment, via a wireless tracking system, and calculates a product-shopper proximity measure based at least in part on a physical distance of a shopper traveling along the shopping path from the position of the product.
U.S. Pat. No. 6,317,718 to Fano discloses a system that utilizes a Personal Digital Assistant (PDA)-based, Global Positioning System (GPS)-enabled information gathering agent to create a customized offer information summary for a user based on the location of the user and one or more items of interest. One or more items of interest are obtained from the user, and the physical location of the user is determined. A query based on the items of interest and the physical location of the user is then created, and an information network is queried utilizing this query. A customized offer is received from a retailer-based agent in response to the query, and the customized offer information associated with the items of interest and their locations relative to the physical location of the user is displayed.
Despite the potential advantages of such systems, further consumer information collection and utilization features may be desirable to benefit from consumer behavior patterns and tendencies in some applications.